Combining Singular Value Decomposition and a multi-classifier: A new approach to support coreference resolution

In this paper a new machine learning approach is presented to deal with the coreference resolution task. This approach consists of a multi-classifier system that classifies mention-pairs in a reduced dimensional vector space. The vector representation for mention-pairs is generated using a rich set of linguistic features. The (Singular Value Decomposition) SVD technique is used to generate the reduced dimensional vector space. The approach is applied to the OntoNotes v4.0 Release Corpus for the column-format files used in CONLL-2011 coreference resolution shared task. The results obtained show that the reduced dimensional representation obtained by SVD is very adequate to appropriately classify mention-pair vectors. Moreover, it can be stated that the multi-classifier plays an important role in improving the results.